Adaptive Attention with Consumer Sentinel for Movie Box Office Prediction
To improve the movie box office prediction accuracy, this paper proposes an adaptive attention with consumer sentinel (LSTM-AACS) for movie box office prediction. First, the influencing factors of the movie box office are analyzed. Tackling the problem of ignoring consumer groups in existing predict...
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| Format: | Article |
| Language: | English |
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Wiley
2020-01-01
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| Series: | Complexity |
| Online Access: | http://dx.doi.org/10.1155/2020/6689304 |
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| _version_ | 1832550049011204096 |
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| author | Kaicheng Feng Xiaobing Liu |
| author_facet | Kaicheng Feng Xiaobing Liu |
| author_sort | Kaicheng Feng |
| collection | DOAJ |
| description | To improve the movie box office prediction accuracy, this paper proposes an adaptive attention with consumer sentinel (LSTM-AACS) for movie box office prediction. First, the influencing factors of the movie box office are analyzed. Tackling the problem of ignoring consumer groups in existing prediction models, we add consumer features and then quantitatively analyze and normalize the box office influence factors. Second, we establish an LSTM (Long Short-Term Memory) box office prediction model and inject the attention mechanism to construct an adaptive attention with consumer sentinel for movie box office prediction. Finally, 10,398 pieces of movie box office dataset are used in the Kaggle competition to compare the prediction results with the LSTM-AACS model, LSTM-Attention model, and LSTM model. The results show that the relative error of LSTM-AACS prediction is 6.58%, which is lower than other models used in the experiment. |
| format | Article |
| id | doaj-art-20706a95d47344d19fbb29468cd12de4 |
| institution | Kabale University |
| issn | 1076-2787 1099-0526 |
| language | English |
| publishDate | 2020-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Complexity |
| spelling | doaj-art-20706a95d47344d19fbb29468cd12de42025-02-03T06:07:41ZengWileyComplexity1076-27871099-05262020-01-01202010.1155/2020/66893046689304Adaptive Attention with Consumer Sentinel for Movie Box Office PredictionKaicheng Feng0Xiaobing Liu1School of Economics and Management, Dalian University of Technology, Dalian 116024, ChinaSchool of Economics and Management, Dalian University of Technology, Dalian 116024, ChinaTo improve the movie box office prediction accuracy, this paper proposes an adaptive attention with consumer sentinel (LSTM-AACS) for movie box office prediction. First, the influencing factors of the movie box office are analyzed. Tackling the problem of ignoring consumer groups in existing prediction models, we add consumer features and then quantitatively analyze and normalize the box office influence factors. Second, we establish an LSTM (Long Short-Term Memory) box office prediction model and inject the attention mechanism to construct an adaptive attention with consumer sentinel for movie box office prediction. Finally, 10,398 pieces of movie box office dataset are used in the Kaggle competition to compare the prediction results with the LSTM-AACS model, LSTM-Attention model, and LSTM model. The results show that the relative error of LSTM-AACS prediction is 6.58%, which is lower than other models used in the experiment.http://dx.doi.org/10.1155/2020/6689304 |
| spellingShingle | Kaicheng Feng Xiaobing Liu Adaptive Attention with Consumer Sentinel for Movie Box Office Prediction Complexity |
| title | Adaptive Attention with Consumer Sentinel for Movie Box Office Prediction |
| title_full | Adaptive Attention with Consumer Sentinel for Movie Box Office Prediction |
| title_fullStr | Adaptive Attention with Consumer Sentinel for Movie Box Office Prediction |
| title_full_unstemmed | Adaptive Attention with Consumer Sentinel for Movie Box Office Prediction |
| title_short | Adaptive Attention with Consumer Sentinel for Movie Box Office Prediction |
| title_sort | adaptive attention with consumer sentinel for movie box office prediction |
| url | http://dx.doi.org/10.1155/2020/6689304 |
| work_keys_str_mv | AT kaichengfeng adaptiveattentionwithconsumersentinelformovieboxofficeprediction AT xiaobingliu adaptiveattentionwithconsumersentinelformovieboxofficeprediction |